Reduce RAG costs on Amazon Bedrock with query-aware compression
RAG costs eating your budget? A two-model compression pattern on Bedrock can cut input tokens by 40%+ while keeping answer quality intact.
Why it matters
Amazon Bedrock users running RAG at scale now have a practical, production-ready pattern to slash token costs without building custom infrastructure. This is the kind of engineering optimization that changes unit economics for practitioners.
The key facts
10 to knowQuery-aware compression pattern: smaller model filters retrieved chunks before primary model processes
Cost reduction mechanism: lower input token counts to primary model
Preserves answer quality while reducing spend
Published on AWS ML blog — vendor documentation/pattern, not independent validation
Applicable to any RAG workload on Amazon Bedrock
Pattern: smaller model filters retrieved chunks against query before primary model answers
Goal: reduce input tokens and inference cost while preserving answer quality
Platform: Amazon Bedrock
Use case: RAG at scale where input token costs are material to total cost
Technique: query-aware context compression
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model…